University of California at Santa Barbara Christo Wilson , Bryce Boe, Alessandra Sala, Krishna P. N. Puttaswamy, and Ben

1. User Interactions in Social Networks and their Implications University of California at Santa Barbara Christo Wilson, Bryce Boe, Alessandra Sala, Krishna P. N. Puttaswamy, and Ben Zhao

2. Social Networks University of California at Santa Barbara

3. Social Applications • Enables new ways to solve problems for distributed systems • Social web search • Social bookmarking • Social marketplaces • Collaborative spam filtering (RE: Reliable Email) • How popular are social applications? • Facebook Platform – 50,000 applications Popular ones have >10 million users each University of California at Santa Barbara

4. Social Graphs and User Interactions • Social applications rely on • Social graph topology • User interactions • Currently, social applications evaluated just using social graph • Assume all social links are equally important/interactive • Is this true in reality? • Milgram’s familiar stranger • Connections for ‘status’ rather than ‘friendship’ • Incorrect assumptions lead to faulty application design and evaluation University of California at Santa Barbara

5. Goals • Question: Are social links valid indicators of real user interaction? • First large scale study of Facebook • 10 million users (15% of total users) / 24 million interactions • Use data to show highly skewed distribution of interactions • <1% of people on Facebook talk to >50% of their friends • Propose new model for social graphs that includes interaction information • Interaction Graph • Reevaluate existing social application using new model • In some cases, break entirely University of California at Santa Barbara

6. Outline Characterizing Facebook Analyzing User Interactions Interaction Graphs Effects on Social Applications University of California at Santa Barbara

7. Crawling Facebook for Data • Facebook is the most popular social network • Crawling social networks is difficult • Too large to crawl completely, must be sampled • Privacy settings may prevent crawling • Thankfully, Facebook is divided into ‘networks’ • Represent geographic regions, schools, companies • Regional networks are not authenticated University of California at Santa Barbara

8. Crawling for Data, cont. • Crawled Facebook regional networks • 22 largest networks: London, Australia, New York, etc • Timeframe: March – May 2008 • Start with 50 random ‘seed’ users, perform BFS search • Data recorded for each user: • Friends list • History of wall posts and photo comments • Collectively referred to as interactions • Most popular publicly accessible Facebook applications University of California at Santa Barbara

9. High Level Graph Statistics • Based on Facebook’s total size of 66 million users in early 2008 • Represents ~50% of all users in the crawled regions • ~49% of links were crawlable • This provides a lower bound on the average number of in-network friends • Avg. social degree = ~77 • Low average path length and high clustering coefficient indicate Facebook is small-world 1. A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In Proc. of IMC, October 2007. University of California at Santa Barbara

10. Outline Characterizing Facebook Analyzing User Interactions Interaction Graphs Effects on Social Applications University of California at Santa Barbara

11. Analyzing User Interactions • Having established that Facebook has the expected social graph properties… • Question: Are social links valid indicators of real user interaction? • Examine distribution of interactions among friends University of California at Santa Barbara

12. Distribution Among Friends • Social degree does not accurately predict human behavior • Initial Question: Are social links valid indicators of real user interaction? • Answer: NO For 50% of users, 70% of interaction comes from 7% of friends. Almost nobody interacts with more than 50% of their friends! For 50% of users, 100% of interaction comes from 20% of friends. University of California at Santa Barbara

13. Outline Characterizing Facebook Analyzing User Interactions Interaction Graphs Effects on Social Applications University of California at Santa Barbara

14. A Better Model of Social Graphs • Answer to our initial question: • Not all social links are created equal • Implication: can not be used to evaluate social applications • What is the right way to model social networks? • More accurately approximate reality by taking user interactivity into account • Interaction Graphs • Chun et. al. IMC 2008 University of California at Santa Barbara

15. Interaction Graphs • Definition: a social graph parameterized by… • n : minimum number of interactions per edge • t : some window of time for interactions • n = 1 and t = {2004 to the present} University of California at Santa Barbara

16. Social vs. Interaction Degree 1:1 Degree Ratio Dunbar’s Number (150) 99% of Facebook Users • Interaction graph prunes useless edges • Results agree with theoretical limits on human social cognition University of California at Santa Barbara

17. Interaction Graph Analysis Do Interaction Graphs maintain expected social network graph properties? • Interaction Graphs still have • Power-law scaling • Scale-free behavior • Small-world clustering • … But, exhibit less of these characteristics than the full social network University of California at Santa Barbara

18. Outline Characterizing Facebook Analyzing User Interactions Interaction Graphs Effects on Social Applications University of California at Santa Barbara

19. Social Applications, Revisited • Recap: • Need a better model to evaluate social applications • Interaction Graphs augment social graphs with interaction information • How do these changes effect social applications? • Sybilguard • Analysis of Reliable Email in the paper University of California at Santa Barbara

20. Sybilguard • Sybilguard is a system for detecting Sybil nodes in social graphs • Why do we care about detecting Sybils? • Social network based games: • Social marketplaces: • How Sybilguard works • Key insight: few edges between Sybils and legitimate users (attack edges) • Use persistent routing tables and random walks to detect attack edges University of California at Santa Barbara

21. Sybilguard Algorithm Step 1: Bootstrap the network. All users exchange signed keys. Key exchange implies that both parties are human and trustworthy. Step 2: Choose a verifier (A) and a suspect (B). A and B send out random walks of a certain length (2). Look for intersections. A knows B is not a Sybil because multiple paths intersect and they do so at different nodes. B A University of California at Santa Barbara

22. Sybilguard Algorithm, cont. B A University of California at Santa Barbara

23. Sybilguard Caveats • Bootstrapping requires human interaction • Evaluating Sybilguard on the social graph is overly optimistic because most friends never interact! • Better to evaluate using Interaction Graphs University of California at Santa Barbara

24. Expected Impact Fewer of edges, lower clustering lead to reduced performance Why? Self-loops B A University of California at Santa Barbara

25. Sybilguard on Interaction Graphs • When evaluated under real world conditions, performance of social applications changes dramatically University of California at Santa Barbara

26. Conclusion • First large scale analysis of Facebook • Answer the question: Are social links valid indicators of real user interaction? • Formulate new model of social networks: Interaction Graphs • Demonstrate the effect of Interaction Graphs on social applications • Final takeaway: when building social applications, use interaction graphs! University of California at Santa Barbara

27. Questions? Anonymized Facebook data (social graphs and interaction graphs) will be available for download soon at the Current Lab website! http://current.cs.ucsb.edu/facebook University of California at Santa Barbara

28. Social Networks • Social Networks are popular platforms for interaction, communication and collaboration • > 110 million users • 9th most trafficked site on the Internet • > 170 million users • #1 photo sharing site • 4th most trafficked site on the Internet • 114% user growth in 2008 • > 800 thousand users • 1,689% user growth in 2008 University of California at Santa Barbara

29. High Level Graph Statistics • Based on Facebook’s total size of 66 million users in early 2008 • Represents ~50% of all users in the crawled regions • ~49% of links were crawlable • This provides a lower bound on the average number of in-network friends • Avg. social degree = ~77 • Clustering Coefficient measures strength of local cliques • Measured between zero (random graphs) and one (complete connectivity) • Social networks display power law degree distribution • Alpha is the curve of the power law • D is the fitting error 1. A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In Proc. of IMC, October 2007. University of California at Santa Barbara

30. Social Degree CDF University of California at Santa Barbara

31. Nodes vs. Total Interactions Top 10% of most interactive users are responsible for 85% of total interactions • Social degree does not accurately predict human behavior • Interactions are highly skewed towards a small percent of the Facebook population Top 10% of most well connected users are responsible for 60% of total interactions University of California at Santa Barbara